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Aneuploidy in pancreatic intraepithelial neoplasia. / Alina Raluca Morhan

Swansea University Author: Alina Raluca Morhan

Abstract

Pancreatic ductal adenocarcinoma has one of the most unfavourable prognoses and survival patterns. Pancreatic intraepithelial ductal neoplasias (PanINs) 1-3 are microscopic precursors which display genetic and epigenetic changes leading to adenocarcinoma. We investigated the chromosomal numeric chan...

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Published: 2015
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
URI: https://cronfa.swan.ac.uk/Record/cronfa42775
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Abstract: Pancreatic ductal adenocarcinoma has one of the most unfavourable prognoses and survival patterns. Pancreatic intraepithelial ductal neoplasias (PanINs) 1-3 are microscopic precursors which display genetic and epigenetic changes leading to adenocarcinoma. We investigated the chromosomal numeric changes for chromosomes 1, 6, 9 and 18 using fluorescence in situ hybridization in the progressing intraepithelial neoplasias and the corresponding tumours. We also assessed the protein levels for mitotic checkpoint proteins Mad2 and BubRl using immunohistochemistry and applied correlation models to derive potential significant correlations to chromosome numeric anomalies and clinicopathological parameters. The results revealed that, for the chromosomes included in the study, the average numeric anomalies (aneuploidy) increases with the advancing histological stage. Moreover, the extra copy anomalies (amplifications) were significantly higher in the final precancerous stage (PanIN 3) and/or the cancer stage (p-value<0.05). The protein levels for Mad2 and BubRl did not show a direct correlation to the aneuploidy levels but that could be due to the diversity of the individual tumour makeup and the limited specimens included in the analysis. In the current trend towards personalized genetic therapy for aggressive tumours, the aneuploidy levels and the mitotic checkpoint protein levels could potentially be incorporated in a panel of biomarkers used to predict prognoses and survival patterns.
Keywords: Medicine.
College: Faculty of Medicine, Health and Life Sciences

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